class_docstr: The AUCMeter measures the area under the receiver-operating characteristic
  (ROC) curve for binary classification problems. The area under the curve (AUC)
  can be interpreted as the probability that, given a randomly selected positive
  example and a randomly selected negative example, the positive example is
  assigned a higher score by the classification model than the negative example.
  
  The AUCMeter is designed to operate on one-dimensional Tensors `output`
  and `target`, where (1) the `output` contains model output scores that ought to
  be higher when the model is more convinced that the example should be positively
  labeled, and smaller when the model believes the example should be negatively
  labeled (for instance, the output of a signoid function); and (2) the `target`
  contains only values 0 (for negative examples) and 1 (for positive examples).
code: def __init__(self):
      super(AUCMeter, self).__init__()
      self.reset()
  
file_path: pywick/pywick/meters/aucmeter.py
imports: import numbers
  from .None import meter
  import numpy
  import torch
label_desc: Use image node_id 1 to create a new AUCMeter object from inherited base classes: meter with example: obj = AUCMeter()